Case Study: Rezolve Ai reduces search latency with MongoDB Atlas

Rezolve AI reduces search latency by 50% with MongoDB

Rezolve AI, a company providing AI-powered e-commerce search and product discovery solutions, faced challenges with its legacy Elasticsearch-based system. It struggled with high operational overhead, an inability to efficiently handle precise part number searches for B2B customers, and mounting technical debt. To modernize its platform, the customer implemented MongoDB Atlas and MongoDB Atlas Search to create a more flexible and performant foundation.

The solution involved building a centralized Data Hub on MongoDB Atlas that uses Change Streams to process real-time product data. This provided Rezolve AI with the necessary keyword search capabilities for precise part number matching and reduced infrastructure management through auto-scaling. As a result, MongoDB helped Rezolve AI achieve up to a 50% reduction in search latency, an average response time of 50 milliseconds, and zero downtime during implementation, all while significantly lowering operational overhead.

Rezolve AI